volume | PIER Journals

Abstract

The Synthetic Aperture Interferometric Radiometer (SAIR) has demonstrated significant potential in Earth remote sensing and radio astronomy. However, most existing imaging methods rely on single-scale visibility function, while SAIR systems typically employ sparse arrays with insufficient sampling, which results in unsatisfactory imaging quality. In this paper, we propose a novel deep learning-based imaging method that addresses this limitation by leveraging multi-scale visibility function. The multi-scale visibility fusion network (MS-VFNet) introduces cross-attention mechanisms in the visibility domain for feature fusion across different scales, fully exploiting the implicit structural information, and subsequently reconstructs the brightness temperature images through a dedicated reconstruction module. The simulation results demonstrate that the proposed MS-VFNet achieves superior reconstruction accuracy and image quality compared to state-of-the-art methods, further validating the feasibility of multi-scale fusion in SAIR super-resolution imaging.

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Dr. Fuxin Cai

Professor Jianfei Chen

Dr. Ziang Zheng

Dr. Leilei Liu